To Save Pharma R&amp;D, David Grainger Says Drug Developers Must Think Like CEOs Of Lean Startups

A fundamental problem in pharma R&D is that drug development is so expensive: failures are frequent (most projects fail) and each one is costly.

This problem has become more severe in the context of several important external factors, including a challenging reimbursement environment that demands (not unreasonably) either novelty or strong evidence that a new product using an established mechanism is meaningfully better than existing products – i.e. evidence to support a compelling value story.

Regulatory hurdles (some more reasonable than others, and often specific to particular FDA divisions or even individuals) critically impact the cost and time for new drug development as well.

Pharma has not yet found an effective way to deal with this productivity crisis, and has struggled to replace products facing patent expiration.

One category of approaches involves picking potential research projects more "intelligently,” generally using some hot new technology; there was a time when genetics, or perhaps genetics plus biomarkers, were going to save the industry; outside of a few companies, you don’t really hear this much anymore, as most have learned that, as Brown and Goldstein wrote, “a gene sequence is not a drug,” and disease tends to be remarkably complicated. Furthermore, companies have learned that developing and validating biomarkers can often prove as complicated and challenging as drug development itself.

A second approach (beloved by consultants) involves implementing process improvements, as if the basic problem is one of industrial efficiency, like an underperforming widget factory. Metrics are tracked, productivity is measured, screws are turned – yet on the whole, it’s unclear this has done much good either, as the marginal gains in efficiency may well have been more than offset by the significant losses in creativity and spirit, and in any case have clearly not solved the fundamental problem. At best, these suggestions may have enabled some companies to stay in the game longer (in some cases, perhaps longer than they should have).

A more dramatic approach, memorably codified by analyst Andrew Baum back when he was at Morgan Stanley (he’s now at Citi), urged pharma to strongly consider a “Search and Develop” strategy, especially if their internal R&D wasn’t very strong. This dovetailed nicely with a broader interest in the concept of open innovation, and Bill Joy’s concept that most of the smartest people in the world are working for somebody else. This approach seems increasingly popular, and today a greater fraction of pharma pipeline consists of products that originated elsewhere. Pharmas are also exploring variations of this approach, partnering with VCs to invest in novel, generally early-stage assets and concepts.

It’s not difficult to imagine a future where large (and even mid-stage) pharmas take the plunge, and unapologetically jettison their research and perhaps early development, instead focusing exclusively on mid-stage development and later. The pharmaceutical industry would not be the first to embrace such an externalized (some use the term “orchestra leader”) approach.

Problematically, as HBS professor Gary Pisano originally pointed out in a related context, even the externalized approach doesn’t really solve the underlying productivity problem; displacing the problem onto startups merely hides the cost of failure (as they simply go out of business and disappear).

This week, in his (essential) Drug Baron Blog, David Grainger of Index Ventures presented what to my mind is one of the most important discussions of R&D productivity since Andrew Baum’s 2010 report, and offers both an unusually insightful view of the problem, and a provocative take on a potential solution.

Grainger’s central contention is that

“drug development is a stochastic process. That much is indisputable, given the level of failure. Processes that we understand and control fail rarely, if ever. But such as the complexity of biology that even the parts we think we understand relatively well still conceal secrets that can derail a drug development program at the last hurdles.”

Grainger goes on to present a model that concludes that reducing cost of each project is the most effective way to increase R&D productivity, even if this makes decision-making along the way slightly worse.

It’s this last point that makes Grainger’s model and argument so interesting; he basically argues that at the earliest stages of research,

“because early decisions have to be made on the basis of very little data (the model, like real-world early stage drug developers, is operating in what Daniel Kahneman called a ‘low-validity environment’), random chance is an important as the ability to make decisions based on the data that has been revealed up to that point.”

In other spends, spending more money at the early stages to get you a bit more information could well be wasted, given the enormity of what you still don’t know.

As Grainger explained to me in an email,

“So many projects fail for reasons that couldn't have been predicted earlier no matter how much you spent on early-stage experiments. It’s not about the quality of your decision making if none of the preclinical data you could collect can predict the failure in an expensive Ph2a study. Once you accept that, you have to change your approach. Spending more on data that doesn't improve the quality of your decision only decreases the number of projects you can progress, and reduces the return on capital when and if you do deliver a winner. So the message from the models is not that you should cut costs no matter what, but really that you have to ask whether the data you will gain is worth the cost in terms of its predictive power for future outcomes.”

He continues, “Mostly, people over-value the data they get. It gives them confidence, it makes them feel they are operating in a high-validity environment. But it’s an illusion.”

While Grainger’s proposed solution – reduce the cost per project – is not novel, I think his concept that it’s acceptable to achieve this at the cost of slightly worse decisions may be a vitally important contribution, especially given the usual imperatives to make the best possible decisions you can, and (especially in large companies), avoid being wrong, and covering your butt whenever possible.

In essence, Grainger is suggesting that many drug developers have become accustomed to acquiring excess information, data that helps them feel more confident, but that doesn’t significantly modulate the ultimately probably of success.

While acknowledging that the increased need for a value story means that drug approval doesn’t ensure commercial success (something that used to be more of a given), he’s skeptical of the (now very trendy) focus on articulating market access plans at the earliest stages of an R&D project.

Many companies, he wrote to me,

“Wanted to build stronger and stronger cases for early stage projects. They wanted to discharge all the commercial risk as well as the technical risk by building ever more ‘complete’ early data packages. And the cost goes up and up as a result. But of course you can't discharge commercial risk until you know the clinical profile of the compound (and, often, not even until you see the approved label!). It often drives management teams (and even investors) to build commercial models for preclinical assets - and not just on the back of an envelope, either!”

Grainger adds, “To progress an early stage asset, you have to believe there is a great commercial opportunity if the technical risk plays out as you hope. But going as far as having a market access plan for a preclinical asset is bonkers. You can waste money assembling such a thing, but it’s useless because there are way too many unknowns.”

The answer, Grainger contends, are a hypothesized breed of “rock star” drug developers who think like the CEOs of lean startups, and excel specifically at “cutting out superfluous experiments,” and possess the “ability to incisively choose the ‘best’ minimum set of experiments that yield the maximum predictive power available for the least dollars.”

He’s also a strong believer in these drug developers having real skin in the game – literally committing several years of their own careers, potentially, to the specific project they endorse (discussed here and here as well).

There are a couple of obvious problems here, of course. For starters, it does sound a bit like the “assume a can opener” economics quip; how very convenient to hypothesize a person who can offer the exact skills your problem requires.

It’s even more convenient – and evocative of Camelot’s “C’est Moi” -- when you realize that a key premise of Index Ventures is the ability to identify just these rock stars, who they call “Index Drug Developers.”

Grainger may be onto something, however. The startup space is dotted with examples of lean teams of drug developers who serially coalesce around a promising asset or series of assets, develop one to a value-inflection point, exit, and then repeat the process (or try to).

However, it’s hard to know whether these drug developers – or whether anyone – actually have the traits Grainger describes, or whether we just remember the lucky few who happen to get it right (exactly the same challenge Kahneman describes in context of investors).

As if identifying rock star drug developers wasn’t enough of a challenge, it’s interesting to contemplate how such a lean drug development approach could ever be implemented in a pharmaceutical company, given the organizational challenges.

To be more blunt: I can’t imagine this happening successfully at any large or medium-sized drug company I know; the skill-sets – especially the need to embrace uncertainty and the need to make imperfect decisions under conditions of incomplete information, and to not just accept but really own a very high failure rate -- seem so different, and often fundamentally at odds with how information is gathered and decisions are approached today.

I’d like to think (and acknowledge my bias here) that small, agile companies, perhaps, have at least a chance to get this right, though this remains to be seen.

Do the talented drug developers Grainger imagines even exist? We better hope so, given that our ability to produce innovative new medicines requires either fewer failures – requiring us to be far smarter than we are now -- or a new approach to failures, one that expects and tolerates them, so long as they are achieved significantly faster, cheaper, and better than they are today.